# Biometric Template Storage with Blockchain: A First Look into Cost and Performance Tradeoffs

## TL;DR

This paper investigates the practical tradeoffs of storing biometric templates on blockchain, analyzing latency, cost, and biometric performance, and proposes Merkle tree-based methods as effective solutions.

## Contribution

It provides an experimental analysis of blockchain-based biometric storage, highlighting the cost-performance tradeoffs and proposing Merkle trees as a viable approach.

## Key findings

- Direct storage schemes are prohibitively expensive for biometric data.
- Merkle tree-based storage offers a better cost-performance balance.
- Blockchain integration impacts biometric system latency and accuracy.

## Abstract

We explore practical tradeoffs in blockchain-based biometric template storage. We first discuss opportunities and challenges in the integration of blockchain and biometrics, with emphasis in biometric template storage and protection, a key problem in biometrics still largely unsolved. Blockchain technologies provide excellent architectures and practical tools for securing and managing the sensitive and private data stored in biometric templates, but at a cost. We explore experimentally the key tradeoffs involved in that integration, namely: latency, processing time, economic cost, and biometric performance. We experimentally study those factors by implementing a smart contract on Ethereum for biometric template storage, whose cost-performance is evaluated by varying the complexity of state-of-the-art schemes for face and handwritten signature biometrics. We report our experiments using popular benchmarks in biometrics research, including deep learning approaches and databases captured in the wild. As a result, we experimentally show that straightforward schemes for data storage in blockchain (i.e., direct and hash-based) may be prohibitive for biometric template storage using state-of-the-art biometric methods. A good cost-performance tradeoff is shown by using a blockchain approach based on Merkle trees.